Salient Knowledge Pathways: Sparse Cross-Modal Routing for Efficient Knowledge-Intensive Multimodal Question Answering

Noor Islam S. Mohammad; Ulu\u{g} Bayaz{\i}t

REVIEW 4 major objections 4 minor 25 references

Across five knowledge-intensive visual QA benchmarks, SKIP claims to match or beat dense retrieval-augmented baselines in accuracy while using 3.4–6.8x fewer FLOPs and 2.7x less latency, by routing computation along question-conditional spa

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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2026-08-01 02:28 UTC pith:2MZAB3VB

load-bearing objection A genuinely useful efficiency architecture with solid ablations — the theory overreaches and the empirical claims need code/data before they can be trusted, but the paper deserves serious peer review. the 4 major comments →

arxiv 2607.25422 v1 pith:2MZAB3VB submitted 2026-07-28 cs.AI

Salient Knowledge Pathways: Sparse Cross-Modal Routing for Efficient Knowledge-Intensive Multimodal Question Answering

Noor Islam S. Mohammad , Ulu\u{g} Bayaz{\i}t This is my paper

classification cs.AI

keywords knowledge-intensive visual question answeringsparse routingvisual token pruningretrieval-augmented generationconditional computationefficient inferencecross-modal attentioninformation bottleneck

verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Knowledge-intensive visual question answering normally pays full cost for visual encoding, retrieval, and fusion on every query, even though most of the image and most retrieved passages are irrelevant to the question. SKIP is a unified inference architecture that routes computation along sparse pathways jointly conditioned on the question, the image, and a predicted difficulty budget. The paper reports that this question-conditional sparsity matches or exceeds the accuracy of strong dense baselines on five benchmarks while using substantially less compute, and it supports the empirical sweet spot with an information-bottleneck bound. If the claim holds, it means retrieval-augmented multimodal QA can be made dramatically cheaper without sacrificing accuracy, and the saved compute can be reinvested into broader retrieval and fusion.

Core claim

The paper's central claim is that knowledge-intensive multimodal question answering is far more sparse than current systems assume: most visual tokens and most retrieved passages are irrelevant to any given question, so computation can be routed along question-conditional pathways. SKIP prunes visual tokens before retrieval (QVS), issues a separate retrieval query per salient image region (RCSR), restricts cross-modal attention to a learned bipartite edge set (BSCA), lets a difficulty predictor set per-query budgets (DBC), and lets a small drafter answer easy queries without retrieval (SKV). The paper reports that this cascade matches or exceeds the accuracy of strong dense baselines across

What carries the argument

The carrying mechanism is a compositional cascade of five learned components arranged so each stage's sparse output feeds the next: QVS, a cross-attention scorer that prunes visual tokens conditioned on the question before any retrieval; RCSR, which clusters retained tokens into regions and issues per-region sparse retrieval queries; BSCA, a bipartite sparse cross-attention operator that computes attention only on edges above a learned threshold; DBC, a small MLP that predicts per-query visual and retrieval budgets; and SKV, a 700M-parameter drafter that speculatively answers easy queries and routes the rest to the full pipeline. The theoretical anchor is Theorem 4.3, an information-bottlene

Load-bearing premise

The central claim collapses if the accuracy, FLOPs, and latency comparisons in Table 1 are not apples-to-apples: the paper asserts a shared Vicuna-7B backbone but provides no reimplementation details for the baselines, and the appendix itself flags an unresolved roughly 8x discrepancy in the reported training compute.

What would settle it

Re-run Table 1 under the paper's stated protocol—same Wikipedia/Wikidata corpus, EV A-CLIP-G vision encoder, Vicuna-7B backbone, LoRA adapters, and A100-80GB timing—for SKIP and each dense baseline, and check whether the reported accuracy, FLOPs, and latency deltas reproduce. Separately, audit the training logs against the appendix's conflicting figures of ~4,800 A100-hours versus ~576 GPU-hours; even if the routing idea survives, that discrepancy would signal that the reported empirical surface is not yet trustworthy.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

If SKIP is correct, existing retrieval-augmented visual QA systems can cut inference cost by 3.4–6.8x without losing accuracy, and the freed compute can be used for larger retrieval corpora or richer fusion.
On entity-centric benchmarks (InfoSeek +4.3, Encyclopedic-VQA +3.4), per-region retrieval delivers the largest gains, indicating that small image regions carrying entity identity are the key bottleneck dense global retrieval misses.
The reported KV-cache reduction (from roughly 12GB to under 5GB for a 7B backbone) would make knowledge-intensive multimodal QA feasible on consumer 16GB GPUs.
The speculative verification fast path routes 34–41% of queries through a cheap drafter with negligible reported accuracy loss, concentrating expensive retrieval and fusion on genuinely hard questions.
Sparse routing is most valuable under tight compute budgets: at 0.20x the strongest baseline's cost, SKIP is reported to retain 98.4% of its full-budget accuracy while the dense baseline drops to 87.1% of its peak.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

If the O(1/sqrt(N)) sparsity bound generalizes beyond still images, the same question-conditional routing logic should extend to video QA, where temporal sparsity adds another dimension that region-style clustering could exploit.
DBC and SKV operate on global image–question embeddings with no task-specific inductive bias, so they could likely be grafted onto existing dense retrieval-augmented VLMs as lightweight post-hoc wrappers without retraining the backbone.
The paper's own failure analysis points to long-tail entity coverage as the dominant remaining error source, so improving the retrieval corpus itself may yield larger accuracy gains than further sparsity tuning.
A clean testable extension is to deploy SKIP with the retrieval path disabled on purely perceptual tasks; the paper reports only a 1.1x speedup there versus 2.5–2.9x on knowledge-intensive benchmarks, confirming that the efficiency gains are specific to retrieval-heavy settings.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Desk Editor's Note

A genuinely useful efficiency architecture with solid ablations — the theory overreaches and the empirical claims need code/data before they can be trusted, but the paper deserves serious peer review.

read the letter

Take a look at arXiv:2607.25422. This is a real architecture paper, and the parts that need scrutiny are not where you'd first expect.

What's new: SKIP composes five known ideas — question-conditioned visual token pruning, per-region retrieval, sparse bipartite cross-attention, a difficulty-based compute controller, and a speculative routing drafter — into one pipeline where each stage's sparsity conditions the next. That composition is the contribution, and the ablations back it up fairly well: random pruning instead of QVS costs 9 points, global retrieval costs 3.5, dense attention triples FLOPs for a negligible gain, and the single-component rows all land below the full system. The appendices are unusually honest: negative results, a long-tail failure taxonomy, and an explicit flag of an unreconciled ~8x discrepancy in reported training compute (Appendix K). That last item is a good sign of author integrity, but it also means the numbers aren't ready for prime time.

Soft spots, in proportion. The theory is the biggest overclaim. Theorem 4.3's proof in Appendix C.1 doesn't deliver the stated O(sqrt(V) log(1/eps)) bound. Step 4's Chernoff-style concentration treats per-token MI contributions as bounded independent random variables — no justification for independence or the mean, and the algebra gives |S| >= sqrt(C0 V log(1/eps))/eps, which is substantially weaker in its epsilon dependence than the claimed sqrt(V) log(1/eps). Step 1's ranking argument requires a positive score gap Delta, and no lower bound on Delta is provided. Appendix C.2 acknowledges the assumptions are strong ('reviewer concern'), and Appendix N validates Assumption 4.2 with a counterfactual log-probability proxy, which by the data-processing inequality is a lower bound, not an upper bound. So the theory should be demoted to an empirical observation or fixed; it is not load-bearing for the empirical claim.

The empirical claim itself is plausible but unverifiable as it stands: no code, no error bars in the main table, and baseline comparability is asserted — RA-CM3 is not natively Vicuna-based, and no reimplementation details are given. The compute-hours flag makes you want to see training logs. These are addressable issues, not demonstrated errors.

Bottom line: the architecture deserves serious referee time. If the numbers hold, it's a solid within-subfield advance. I would send this to peer review with a request for code/data, error bars, a corrected or de-emphasized theory, and a reconciliation of the compute figures. I wouldn't cite the numbers until the code appears, but I'd cite the design in related work.

Referee Report

4 major / 4 minor

Summary. SKIP is a sparse, question-conditional routing architecture for knowledge-intensive multimodal QA. It combines five components — QVS visual-token pruning, RCSR per-region retrieval, BSCA sparse bipartite cross-attention, DBC difficulty-adaptive budgeting, and SKV speculative fast-path verification — and claims a theoretical guarantee (Theorem 4.3) that retaining O(sqrt(V) log(1/epsilon)) visual tokens preserves question-answer mutual information up to epsilon. On five benchmarks (OK-VQA, A-OKVQA, InfoSeek, Encyclopedic-VQA, ViQuAE) the paper reports accuracy at or above dense retrieval-augmented baselines while using 3.4–6.8x fewer FLOPs and 2.7x less latency. The empirical result, if reproducible under controlled comparison, would be practically significant; the theoretical and comparison evidence as presented is not yet sufficient.

Significance. The problem is important: KI-MMQA pays dense costs for visual tokens, retrieval, and cross-modal fusion even for easy queries, and the proposed architecture directly targets all three. The component design is well-motivated, and the paper includes unusually extensive ablations, failure-mode analyses, and memory/latency breakdowns. If the main-table accuracy and efficiency numbers are confirmed with controlled baselines and released code, SKIP would be a meaningful advance in efficient retrieval-augmented VQA. However, the paper's central theoretical claim is not established by the proof in Appendix C, and the empirical comparison is currently unverifiable: no code or checkpoints are available, baseline reimplementation details are absent, and the main table reports no error bars. The information-bottleneck result is the least reliable part of the paper and should not be used as a headline guarantee.

major comments (4)

[Appendix C.1, Step 4 (Eq. 22-23)] The proof of Theorem 4.3 does not deliver the stated O(sqrt(V) log(1/epsilon)) bound. The Chernoff-style inequality in Eq. (22), after rearrangement, gives |S| >= sqrt(C0 V log(1/epsilon)) / epsilon, not sqrt(V) log(1/epsilon). Moreover, Step 1's ranking argument introduces the score gap Delta, and the final constant is C = max{C0/epsilon^2, 4L eta / Delta}, so C depends on epsilon and on an unquantified Delta, contradicting the theorem's claim that C depends only on L and eta. The per-token MI contributions are also treated as independent bounded random variables without justification. As written, Theorem 4.3, Corollary 4.4, and the Section 5.5 agreement with the empirical sweet spot are unsupported.
[Appendix N and Appendix C.2] The empirical validation of Assumption 4.2 uses a counterfactual log-probability proxy that is a lower bound on the true per-token mutual information by the data-processing inequality. A small value of eta relative to this proxy does not upper-bound the true error |s_i - I(v_i; a | q)|, so Assumption 4.2 is not validated. Similarly, Theorem C.1's 'accuracy guarantee' is largely an accounting of assumed quantities: Assumption C.1 assumes 95% retained MI, Assumption C.2 assumes retrieval miss <=12%, and Lemma C.1 assumes SKV F1 >= 0.90. The resulting bound is O(epsilon_v + delta_r + 0.10), not an independent guarantee. The abstract's 'retained accuracy guarantees' overstates what is proven.
[Section 5.1, Table 1] The main empirical claim requires an apples-to-apples comparison, but the paper only states that all baselines share a Vicuna-7B backbone. RA-CM3's original architecture is not Vicuna-based, and no reimplementation details, evaluation harness, or per-baseline timing protocol are provided. If Table 1 mixes published accuracy numbers with SKIP's own runs, the accuracy and latency deltas are uncontrolled. The main table also lacks error bars or seed-specific standard deviations; the statement in Appendix K that variance is <=0.4 points for all main-table entries is not accompanied by the per-benchmark values. Code and checkpoints are said to be 'available' at a URL, but Appendix K.4 states they will be released upon acceptance, so the results cannot currently be reproduced or independently verified.
[Appendix K and Appendix D] There is an unresolved internal inconsistency in the reported experimental setup. The main text (Section 5.1) and Table 1 describe a 7B model with EVA-CLIP-G, 576 visual tokens, and ~4,800 A100-hours of training. Appendix D and F describe a different configuration (LLaVA-1.6-13B, CLIP ViT-L/14, L=256, rho_v=0.40), and Appendix K explicitly flags an unresolved ~8x discrepancy between ~4,800 A100-hours and ~576 GPU-hours. It is therefore unclear which configuration produced the main-table numbers. This must be reconciled before the empirical claim can be assessed.

minor comments (4)

[References] The Gumbel-softmax citation in Section 3.3 is listed as 'Jang, S. Cultural brokerage and creative performance in multicultural teams' (Organization Science). The intended reference is Jang, Gu, and Poole, 'Categorical Reparameterization with Gumbel-Softmax' (ICLR 2017).
[Figure 4] The x-axis label in Figure 4 appears garbled ('5·10 −20.1 0.15 0.2...'); the axis ticks and labels should be cleaned up.
[Appendix D.8] The text introduces a 'submission-time hyperparameter' configuration and a separate 'expanded backbone-ablation configuration' with different retention rates, backbones, and FLOPs. This is confusing and should be reconciled with Section 5 and Table 1, not only flagged as a discrepancy.
[Section 5.2] The phrase 'at matched FLOPs (0.42 TFLOPs, the BLIP-2 budget)' is confusing because 0.42 TFLOPs is also SKIP's reported FLOPs in Table 1. Clarify whether SKIP is being compared at its own operating point or at a matched reduced budget.

Circularity Check

3 steps flagged

The √V sparsity theorem and the 'retained accuracy guarantee' are constructed from assumptions that already contain the conclusions; the predicted sweet spot uses a post-hoc constant C. The benchmark/ablations themselves are not circular.

specific steps

self definitional [Appendix C.1, Step 4 (proof of Theorem 4.3); also Section 4 proof sketch]
"We make the realistic empirical observation (verified in Appendix N) that the per-token mutual information distribution is heavy-tailed, with the top √V tokens carrying most ≥1−O(ε) the total mutual information. Formally, we can apply a concentration inequality: viewing each non-selected token's contribution as a bounded random variable with mean O(L/V), the sum over V−V′ such tokens concentrates around its mean with a Chernoff-style tail:"

This 'empirical observation' is the theorem's conclusion in different words: it asserts that the top √V tokens already contain 1−O(ε) of I(V;a|q), which is exactly what Theorem 4.3 claims to prove at the √V scale. The subsequent Chernoff tail only repackages this assumed heavy-tail concentration into Eq. (22); nothing in Assumptions 4.1–4.2 or Steps 1–3 forces a √V·log(1/ε) rate. Appendix N, cited as verification, actually measures only η (the QVS score error) and a Spearman correlation, not the heavy-tail claim. Hence the bound's central √V factor is an input rather than a derived result.
fitted input called prediction [Section 5.5 / Corollary 4.4 / Section 4 'non-vacuous' paragraph]
"Corollary 4.4. For typical V=576 (a 24×24 patch grid), retaining V′=48–72 tokens (8–12.5%) suffices for ε=0.05, matching our empirical findings (Section 5). ... The empirical sweet spot is consistent with our O(1/√V) theoretical prediction: √576/576≈0.042, with the bound's constant C∈[1.5,3] placing the sweet spot in the 0.06–0.13 range."

The 'prediction' is made using a free constant C∈[1.5,3] whose value is never derived from L, η, C0, or Δ. The paper fixes C only after observing the empirical optimum (V′=64, i.e., 11% retention) and then declares that this C 'places' the bound on the measured 0.06–0.13 interval. A bound with a post-hoc constant cannot corroborate the data; it is a restatement of the empirical sweet spot in the theorem's notation. Thus the celebrated agreement between Corollary 4.4 and the experiments is constructed, not tested.
fitted input called prediction [Appendix C.3 Theorem C.1 and Appendix C.4 proof; abstract 'retained accuracy guarantees']
"Theorem C.1 — SKIP Efficiency–Fidelity Trade-off. Under Assumptions C.1–C.3, let Acc(·) denote expected accuracy on P. For any ρv ≥ρ∗v and τν ≤0.5, Acc(SKIP)≥Acc(Dense)−O(εv +δr), ... Term (i) is bounded by εv <0.05 via Assumption C.1."

The advertised 'accuracy guarantee' is an accounting of its own assumptions. Assumption C.1 asserts that the pruned visual set retains (1−εv) of the mutual information; Assumption C.2 asserts that retrieval misses at most δr≤0.12; Lemma C.1 converts the measured SKV F1≥0.90 into a per-passage preservation probability. Substituting these into the proof's decomposition (Eq. 28) returns εv and δr as the accuracy gap, with an additional 0.10 term silently absorbed into the big-O. The 'O(εv+δr)' bound is therefore a relabeling of the assumed information-loss and retrieval-miss rates, not an independent derivation, and cannot serve as the first-principles guarantee claimed in the abstract.

full rationale

The empirical headline — SKIP matches or exceeds dense baselines at 3.4–6.8× fewer FLOPs — is a benchmark result supported by Table 1 and the ablations, and it is not circular; the experiments are self-contained comparisons on public datasets. The circularity is in the theoretical scaffolding. Theorem 4.3's proof imports the target √V retention rate as an 'empirical observation' that the top √V tokens carry 1−O(ε) of the mutual information, and Appendix N does not verify that observation. Corollary 4.4's agreement with the empirical sweet spot is then obtained by choosing the free constant C∈[1.5,3] after the fact. Theorem C.1's accuracy guarantee reduces to Assumptions C.1–C.2 plus the measured SKV F1, with a dropped 0.10 term. These moves make the theory's 'predictions' and 'guarantees' equivalent to their inputs by construction, so the paper's central theoretical contribution is partially circular. The underlying empirical comparison and architecture evaluation remain independent content, which is why the overall score is 6, not 8. No load-bearing self-citation chain is present.

Axiom & Free-Parameter Ledger

8 free parameters · 6 axioms · 0 invented entities

The central empirical claim depends on a set of tuned hyperparameters and on assumptions that the mutual information is concentrated and that QVS tracks per-token MI. The theoretical guarantee is assembled from assumptions that already contain the desired conclusion (95% MI retention, ≤12% retrieval miss) plus a measured F1 number, so the ledger is heavier than the abstract implies.

free parameters (8)

visual retention rate ρ = 0.11 (7B submission config); 0.40 (13B ablation config)
Chosen from validation sweep (Appendix M); the theoretical sweet spot is bracketed afterwards by setting C∈[1.5,3].
per-region top-k' = 4
Tuned; sensitivity moderate; affects retrieval coverage and cost.
BSCA quantile β / edge threshold τ_e = β=0.10, τ_e=0.60
Tuned; low sensitivity on OK-VQA but controls fusion sparsity.
SKV confidence threshold τ_SKV = 0.82 (0.91 for counting)
Calibrated on held-out splits to keep accuracy loss ≤1%; per-category adjustment is post hoc.
DBC budget ranges = V'∈[32,128], K'∈[2,16], median V'=64, K'=6
Learned mapping, but ranges and staged calibration targets are hand-set.
region count R = min(V'/4,8)
Ceiling 8 calibrated to keep retrieval latency <15%; saturation at 8.
loss weights λ1, λ2, λv, λe = 0.5, 0.1, 0.1, 0.05
Tuned; λ1 sensitivity low, but still free choices affecting sparsity.
theory constants L, η, C, C0, Δ = L=1, η≈0.013 (OK-VQA), C∈[1.5,3]; C0,Δ unspecified
η measured via a counterfactual proxy on the system's own scores; C chosen after seeing V'=64 sweet spot; not a parameter-free prediction.

axioms (6)

domain assumption Assumption 4.1: question-conditional MI I(V_S;a|q) is L-Lipschitz in retention rate on intervals of width ≥1/√V
Needed for the √V factor in Theorem 4.3; asserted as 'mild' without proof for real VLMs.
domain assumption Assumption 4.2: QVS scores approximate per-token MI within η
Validated in Appendix N with a log-probability counterfactual proxy on the model itself, not against ground-truth MI.
ad hoc to paper Assumption C.1 (QCVS): there exists a retention rate with ≥95% retained MI
Essentially assumes the conclusion of the sparsity theory; justified by Grad-CAM mass on a 500-instance probe.
domain assumption Assumption C.2: retrieval miss probability ≤0.12
Estimated via oracle passage recall on the same benchmarks; worst case on ViQuAE.
ad hoc to paper Step 4 concentration: per-token MI contributions are bounded random variables with mean O(L/V) and concentrate by Chernoff
No probability space or independence condition is specified; the 'heavy-tailed' observation is empirical from the same QVS proxy.
domain assumption SKV verification F1 ≥0.90
Measured on a held-out verification set; Lemma C.1 converts it into preservation probability 0.90.

pith-pipeline@v1.3.0-alltime-deepseek · 31381 in / 19634 out tokens · 192358 ms · 2026-08-01T02:28:50.196810+00:00 · methodology

0 comments

read the original abstract

Knowledge-intensive multimodal question answering (KI-MMQA) sits at the intersection of three expensive primitives: long visual token sequences, dense retrieval over large external corpora, and full cross-modal fusion. Existing systems pay all three costs uniformly per query, even though only a small fraction of visual content and retrieved knowledge is actually relevant to any given question. We introduce SKIP (Salient Knowledge-Injected Pathways), a unified inference architecture that routes computation along sparse pathways jointly conditioned on the question, the image, and a difficulty estimate. SKIP combines question-guided visual token pruning, region-conditional sparse retrieval, bipartite sparse cross-attention, and speculative knowledge verification with an adaptive budget controller that allocates compute proportional to predicted question difficulty. We derive an information-bottleneck bound showing that the optimal visual sparsity rate scales as $O(1/\sqrt{N})$ under realistic question-image mutual-information assumptions, with retained accuracy guarantees. Across five KI-MMQA benchmarks (OK-VQA, A-OKVQA, InfoSeek, Encyclopedic-VQA, and ViQuAE), SKIP matches or exceeds the accuracy of strong dense baselines while using $3.4$--$6.8\times$ fewer FLOPs and $2.7\times$ less end-to-end latency. Code available at: https://pmlrbd.github.io/skip/

Figures

Figures reproduced from arXiv: 2607.25422 by Noor Islam S. Mohammad, Ulu\u{g} Bayaz{\i}t.

**Figure 1.** Figure 1: Accuracy–compute frontier on OK-VQA. SKIP sustains > 59% accuracy at 5× less compute than dense baselines, and exceeds the strongest baseline at all measured budgets. Modern vision encoders emit 256–2304 patch tokens per image (Dosovitskiy et al., 2021; Radford et al., 2021), flooding the downstream context with spatial detail that is overwhelmingly irrelevant to any given question. Competitive retrieval … view at source ↗

**Figure 2.** Figure 2: SKIP architecture. Solid arrows denote data flow; dashed arrows denote routing/budget control. The QVS module prunes visual tokens conditional on the question and DBC-allocated budget; RCSR retrieves per-region; BSCA fuses sparsely; the SKV drafter short-circuits easy queries. where ∥ the concatenation is and ⊙ is the Hadamard product. The element-wise product term is critical: it captures multiplicative … view at source ↗

**Figure 3.** Figure 3: End-to-end latency on A100-80GB. SKIP is 2.5–2.9× faster than the strongest baseline across all benchmarks. DBC budget allocation, and SKV passage verification) accounts for <5% of total wall-clock time. The remaining latency is distributed between retrieval (∼25%, dominated by FAISS IVF-PQ search across the ∼800M-entry index) and autoregressive decoder generation (∼60%, consistent with the generation-bo… view at source ↗

**Figure 4.** Figure 4: Visual sparsity vs. accuracy on OK-VQA. QVS achieves 99% of the no-pruning accuracy at just 11% retention. The empirical sweet spot matches the O(1/ √ V ) prediction of Theorem 4.3 [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗

**Figure 5.** Figure 5: Full SKIP pipeline. The DBC (orange, dashed arrows) broadcasts the retention ratios ρv and ρr to QVS and RCSR/SKV respectively. The grey arrow denotes the skip-connection that passes Vˆ directly to BSCA alongside the verified passages Kˆ. B.2. Question-Guided Visual Token Pruning (QVS) Motivation. In knowledge-intensive settings most visual tokens (homogeneous background patches) carry no questionrelevant… view at source ↗

discussion (0)

Reference graph

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Liu, H., Li, C., Li, Y ., and Lee, Y . J. Improved baselines with visual instruction tuning. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 26296–26306, 2024a. 10 Salient Knowledge Pathways for Efficient Multimodal QA Liu, H., Li, C., Li, Y ., Li, B., Zhang, Y ., Shen, S., and Lee, Y . J. Llava-next: Improv...

2024
[19]

EV A- CLIP: Improved training techniques for CLIP at scale

Sun, Q., Fang, Y ., Wu, L., Wang, X., and Cao, Y . EV A- CLIP: Improved training techniques for CLIP at scale. In arXiv:2303.15389,

Pith/arXiv arXiv
[20]

the theoretical analysis depends on strong mutual- information-related assumptions whose connection to real VLMs remains unclear

11 Salient Knowledge Pathways for Efficient Multimodal QA Appendices The appendix is organized as follows. Appendix A fixes the notation used throughout this supplement. Appendix B gives complete, self-contained formal definitions of all five SKIP components together with the end-to-end inference algorithm. Appendix C contains the full proof of Theorem 4....

2017
[23]

We use SPLADE’s expansion vocabulary of size 30,522 (matching BERT tokenizer). Backbone.Vicuna-7B v1.5, fine-tuned with LoRA rank-64 adapters on QVS / RCSR / BSCA training data ( ∼650K (image, question, retrieved chunks, answer) tuples assembled from OK-VQA, A-OKVQA, InfoSeek, and Encyclopedic-VQA training splits, plus synthetic augmentation described bel...

2026
[24]

What year was this building completed?

Statistical significance:SKIP’s improvement over RA-CM3 is statistically significant ( p <0.001 via paired bootstrap with10,000resamples) on all five benchmarks. 28 Salient Knowledge Pathways for Efficient Multimodal QA Table 19.Seed variance (Acc.±std) across seeds{2026,42,137}for the LLaV A-1.6-13B backbone-ablation configuration. Benchmark Accuracy (±s...

2026
[2013]

Token merging: Your vit but faster

Bolya, D., Fu, C., Dai, X., Zhang, P., Feichtenhofer, C., and Hoffman, J. Token merging: Your vit but faster. arXiv preprint arXiv:2210.09461, 2023a. URL https: //arxiv.org/abs/2210.09461. Bolya, D., Fu, C.-Y ., Dai, X., Zhang, P., Feichtenhofer, C., and Hoffman, J. Token merging: Your ViT but faster. In International Conference on Learning Representation...

Pith/arXiv arXiv
[2017]

URL https: //aclanthology.org/P17-1171/

doi: 10.18653/v1/P17-1171. URL https: //aclanthology.org/P17-1171/. Chen, X., Wang, X., Beyer, L., Kolesnikov, A., Zhai, X., et al. Pali-x: Scaling multimodal learning with vision- language models.arXiv preprint arXiv:2305.18565, 2023b. doi: 10.48550/arXiv.2305.18565. URL https: //arxiv.org/abs/2305.18565. Chen, Y ., Hu, H., Luan, Y ., Sun, H., Changpinyo...

Pith/arXiv arXiv doi:10.48550/arxiv.2305.18565 1904
[2019]

Dai, W., Li, J., Tan, Z., et al

URL https:// arxiv.org/abs/1904.10509. Dai, W., Li, J., Tan, Z., et al. Instructblip: Towards general-purpose vision-language models with instruction tuning.arXiv preprint arXiv:2305.06500,

Pith/arXiv arXiv 1904
[2020]

org/abs/2004.05150

URL https://arxiv. org/abs/2004.05150. Bengio, Y ., L´eonard, N., and Courville, A. Estimating or propagating gradients through stochastic neurons for con- ditional computation.arXiv preprint arXiv:1308.3432,

Pith/arXiv arXiv 2004
[2021]

Driess, D., Xia, F., Sajjadi, M. S. M., Lynch, C., et al. Palm-e: An embodied multimodal language model.arXiv preprint arXiv:2303.03378,

Pith/arXiv arXiv
[2022]

URL https://arxiv

doi: 10.48550/arXiv.2211.09699. URL https://arxiv. org/abs/2211.09699. Hu, Z., Iscen, A., Sun, C., Wang, Z., Chang, K.-W., Sun, Y ., Schmid, C., Ross, D. A., and Fathi, A. REVEAL: Retrieval-augmented visual-language pre-training with multi-source multimodal knowledge memory. InPro- ceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recog...

Pith/arXiv arXiv doi:10.48550/arxiv.2211.09699 2007
[2023]

URL https://arxiv

doi: 10.48550/arXiv.2308.12966. URL https://arxiv. org/abs/2308.12966. Beltagy, I., Peters, M. E., and Cohan, A. Long- former: The long-document transformer.arXiv preprint arXiv:2004.05150,

Pith/arXiv arXiv doi:10.48550/arxiv.2308.12966 2004
[2024]

Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q., Hinton, G., and Dean, J

URLhttps://arxiv.org/abs/2407.12854. Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q., Hinton, G., and Dean, J. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. arXiv preprint arXiv:1701.06538,

Pith/arXiv arXiv
[4096]

We chunk articles into passages of ≤100 tokens with 20-token overlap, yielding ∼6.5M passages

Retrieval index.SPLADE++ over a Wikipedia-2022 dump. We chunk articles into passages of ≤100 tokens with 20-token overlap, yielding ∼6.5M passages. The inverted index is sharded across 8 CPU workers; per-query retrieval latency is ∼8ms for k′ = 4 and ∼11ms for k′ =

2022

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Qwen-vl: A fron- tier large vision-language model with versatile abili- ties.arXiv preprint arXiv:2308.12966,

Bai, J., Bai, S., Yang, S., Wang, S., Tan, S., Wang, P., Lin, J., Zhou, C., and Zhou, J. Qwen-vl: A fron- tier large vision-language model with versatile abili- ties.arXiv preprint arXiv:2308.12966,

Pith/arXiv arXiv

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is negligible overhead. C.4. Proof of Theorem C.3 Accuracy bound (26).We decompose the accuracy gap: Acc(Dense)−Acc(SKIP) ≤P I( ˆV;A|Q)<(1−ϵ v)I(V;A|Q) | {z } (i) visual information loss +P K∗ ̸⊆ ˆK | {z } (ii) retrieval miss .(28) Term (i) is bounded byϵ v <0.05via Assumption C.1. Term (ii) requires boundingP[K ∗ ̸⊆ ˆK]. Lemma C.1(SKV Preservation of Rel...

2017

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3–5 eggs

Retrieved chunks span ornithology references for the identified species. • BSCA edges: 33/512 survived (6.5%). • Output: “3–5 eggs”. Correct (reference answer: “typically 4”). Example 4 (failure: long-tail).Image: an obscure regional landmark. Q: “In what year was this monument erected?” • QVS correctly retains the monument region (62 tokens). • RCSR retr...

1923

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Accelerating large language model decoding with speculative sampling.arXiv preprint arXiv:2302.01318, 2023a

Chen, C., Borgeaud, S., Irving, G., Lespiau, J.-B., Sifre, L., and Jumper, J. Accelerating large language model decoding with speculative sampling.arXiv preprint arXiv:2302.01318, 2023a. Chen, D., Fisch, A., Weston, J., and Bordes, A. Read- ing wikipedia to answer open-domain questions. In Proceedings of the 55th Annual Meeting of the Asso- ciation for Co...

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URL https://arxiv

doi: 10.48550/arXiv.2305.06500. URL https://arxiv. org/abs/2305.06500. Dao, T., Fu, D. Y ., Ermon, S., Rudra, A., and R´e, C. FlashAt- tention: Fast and memory-efficient exact attention with IO-awareness. InAdvances in Neural Information Pro- cessing Systems (NeurIPS), volume 35, pp. 16344–16359,

Pith/arXiv arXiv doi:10.48550/arxiv.2305.06500

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URL https://arxiv

doi: 10.48550/arXiv.2303.03378. URL https://arxiv. org/abs/2303.03378. Fayyaz, M., Koohpayegani, S. A., Jafari, F. R., Sengupta, S., Joze, H. R. V ., Sommerlade, E., Pirsiavash, H., and Gall, J. Adaptive token sampling for efficient vision transformers. InEuropean Conference on Computer Vision (ECCV), pp. 396–414,

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doi: 10.1609/aaai.v34i07.6782. Hu, X. et al. Promptcap: Prompt-guided image captioning. arXiv preprint arXiv:2301.XXXXX, 2023a. URL https: //arxiv.org/abs/2301.XXXXX. Hu, Y ., Hua, H., Yang, Z., Shi, W., Smith, N. A., and Luo, J. Promptcap: Prompt-guided task-aware image captioning.arXiv preprint arXiv:2211.09699,

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Jang, E., Gu, S., and Poole, B

URL https: //arxiv.org/abs/2007.01282. Jang, E., Gu, S., and Poole, B. Categorical reparameteriza- tion with gumbel-softmax. InInternational Conference on Learning Representations (ICLR),

Pith/arXiv arXiv 2007

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URL https: //doi.org/10.1287/orsc.2017.1162

doi: 10.1287/orsc.2017.1162. URL https: //doi.org/10.1287/orsc.2017.1162. Karpukhin, V ., O˘guz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., and Yih, W.-t. Dense passage retrieval for open-domain question answering. InProceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 6769–6781,

arXiv 2017

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Crafting papers on machine learning

Langley, P. Crafting papers on machine learning. In Langley, P. (ed.),Proceedings of the 17th International Conference on Machine Learning (ICML 2000), pp. 1207–1216, Stan- ford, CA,

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Fast inference from transformers via speculative decoding

Leviathan, Y ., Kalman, M., and Matias, Y . Fast inference from transformers via speculative decoding. InPro- ceedings of the 40th International Conference on Ma- chine Learning (ICML), pp. 19274–19286, 2023a. doi: 10.48550/arXiv.2211.17192. URL https://arxiv. org/abs/2211.17192. Leviathan, Y ., Kalman, M., and Matias, Y . Fast inference from transformers...

Pith/arXiv arXiv doi:10.48550/arxiv.2211.17192

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Liu, H., Li, C., Li, Y ., and Lee, Y . J. Improved baselines with visual instruction tuning. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 26296–26306, 2024a. 10 Salient Knowledge Pathways for Efficient Multimodal QA Liu, H., Li, C., Li, Y ., Li, B., Zhang, Y ., Shen, S., and Lee, Y . J. Llava-next: Improv...

2024

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EV A- CLIP: Improved training techniques for CLIP at scale

Sun, Q., Fang, Y ., Wu, L., Wang, X., and Cao, Y . EV A- CLIP: Improved training techniques for CLIP at scale. In arXiv:2303.15389,

Pith/arXiv arXiv

[14] [20]

the theoretical analysis depends on strong mutual- information-related assumptions whose connection to real VLMs remains unclear

11 Salient Knowledge Pathways for Efficient Multimodal QA Appendices The appendix is organized as follows. Appendix A fixes the notation used throughout this supplement. Appendix B gives complete, self-contained formal definitions of all five SKIP components together with the end-to-end inference algorithm. Appendix C contains the full proof of Theorem 4....

2017

[15] [23]

We use SPLADE’s expansion vocabulary of size 30,522 (matching BERT tokenizer). Backbone.Vicuna-7B v1.5, fine-tuned with LoRA rank-64 adapters on QVS / RCSR / BSCA training data ( ∼650K (image, question, retrieved chunks, answer) tuples assembled from OK-VQA, A-OKVQA, InfoSeek, and Encyclopedic-VQA training splits, plus synthetic augmentation described bel...

2026

[16] [24]

What year was this building completed?

Statistical significance:SKIP’s improvement over RA-CM3 is statistically significant ( p <0.001 via paired bootstrap with10,000resamples) on all five benchmarks. 28 Salient Knowledge Pathways for Efficient Multimodal QA Table 19.Seed variance (Acc.±std) across seeds{2026,42,137}for the LLaV A-1.6-13B backbone-ablation configuration. Benchmark Accuracy (±s...

2026

[17] [2013]

Token merging: Your vit but faster

Bolya, D., Fu, C., Dai, X., Zhang, P., Feichtenhofer, C., and Hoffman, J. Token merging: Your vit but faster. arXiv preprint arXiv:2210.09461, 2023a. URL https: //arxiv.org/abs/2210.09461. Bolya, D., Fu, C.-Y ., Dai, X., Zhang, P., Feichtenhofer, C., and Hoffman, J. Token merging: Your ViT but faster. In International Conference on Learning Representation...

Pith/arXiv arXiv

[18] [2017]

URL https: //aclanthology.org/P17-1171/

doi: 10.18653/v1/P17-1171. URL https: //aclanthology.org/P17-1171/. Chen, X., Wang, X., Beyer, L., Kolesnikov, A., Zhai, X., et al. Pali-x: Scaling multimodal learning with vision- language models.arXiv preprint arXiv:2305.18565, 2023b. doi: 10.48550/arXiv.2305.18565. URL https: //arxiv.org/abs/2305.18565. Chen, Y ., Hu, H., Luan, Y ., Sun, H., Changpinyo...

Pith/arXiv arXiv doi:10.48550/arxiv.2305.18565 1904

[19] [2019]

Dai, W., Li, J., Tan, Z., et al

URL https:// arxiv.org/abs/1904.10509. Dai, W., Li, J., Tan, Z., et al. Instructblip: Towards general-purpose vision-language models with instruction tuning.arXiv preprint arXiv:2305.06500,

Pith/arXiv arXiv 1904

[20] [2020]

org/abs/2004.05150

URL https://arxiv. org/abs/2004.05150. Bengio, Y ., L´eonard, N., and Courville, A. Estimating or propagating gradients through stochastic neurons for con- ditional computation.arXiv preprint arXiv:1308.3432,

Pith/arXiv arXiv 2004

[21] [2021]

Driess, D., Xia, F., Sajjadi, M. S. M., Lynch, C., et al. Palm-e: An embodied multimodal language model.arXiv preprint arXiv:2303.03378,

Pith/arXiv arXiv

[22] [2022]

URL https://arxiv

doi: 10.48550/arXiv.2211.09699. URL https://arxiv. org/abs/2211.09699. Hu, Z., Iscen, A., Sun, C., Wang, Z., Chang, K.-W., Sun, Y ., Schmid, C., Ross, D. A., and Fathi, A. REVEAL: Retrieval-augmented visual-language pre-training with multi-source multimodal knowledge memory. InPro- ceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recog...

Pith/arXiv arXiv doi:10.48550/arxiv.2211.09699 2007

[23] [2023]

URL https://arxiv

doi: 10.48550/arXiv.2308.12966. URL https://arxiv. org/abs/2308.12966. Beltagy, I., Peters, M. E., and Cohan, A. Long- former: The long-document transformer.arXiv preprint arXiv:2004.05150,

Pith/arXiv arXiv doi:10.48550/arxiv.2308.12966 2004

[24] [2024]

Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q., Hinton, G., and Dean, J

URLhttps://arxiv.org/abs/2407.12854. Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q., Hinton, G., and Dean, J. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. arXiv preprint arXiv:1701.06538,

Pith/arXiv arXiv

[25] [4096]

We chunk articles into passages of ≤100 tokens with 20-token overlap, yielding ∼6.5M passages

Retrieval index.SPLADE++ over a Wikipedia-2022 dump. We chunk articles into passages of ≤100 tokens with 20-token overlap, yielding ∼6.5M passages. The inverted index is sharded across 8 CPU workers; per-query retrieval latency is ∼8ms for k′ = 4 and ∼11ms for k′ =

2022